An Assessment of the Canadian Federal-Provincial Crop Production Insurance Program under Future Climate Change Scenarios in Ontario
Bibliographic record
Abstract
Research and observations indicate climate change has and will have an impact on Ontario field crop production. Little research has been done to forecast how climate change might influence the Canadian Federal-Provincial Crop Insurance program, including its premium rates and reserve fund balances, in the future decades. This paper proposes using a mixture of two normal yield probability distribution model to model crop yield conditions under hypothetical climate change scenarios. Then superimposes Crop Insurance premium rate and reserve fund balance calculations onto the yield model to forecast their trends and fluctuation situations in the future decades. We find under the scenarios where climate change alters the probability of a lower yield year occurring and where climate change alters yield averages, both have more significant impacts on premium rates and reserve fund balances, compared to the scenarios where climate change alters yield variations. The results of this research will help Agricorp Ltd. identify the likely frequency and magnitude of both insurance premium rate fluctuations and reserve fund balance fluctuations under different climate change scenarios. Therefore the results can be used to help Agricorp Ltd. identify and forecast both premium rate fluctuation risk and reserve fund liquidity risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".